PT Unknown
AU Volkmar Frinken
   Markus Baumgartner
   Andreas Fischer
   Horst Bunke
TI Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting
BT 13th International Conference on Frontiers in Handwriting Recognition
PY 2012
BP 49
EP 54
AB State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.
ER